Machine Learning

Artificial Intelligence (AI) is transforming the agricultural sector by enabling data‑driven decision‑making and improving farm productivity. Crop yield prediction is one of the most important applications of AI in agriculture because it helps farmers, policymakers, and agricultural planners estimate production and manage resources effectively. This conceptual research paper explores the application of machine learning techniques for crop yield prediction using climate and environmental data. The study discusses the role of historical agricultural datasets such as rainfall, temperature, humidity, soil nutrients, and previous yield statistics in training predictive models. Algorithms such as Decision Trees, Random Forest, and Support Vector Machines are examined for their potential to model complex relationships between environmental factors and crop productivity. The paper proposes a conceptual framework for implementing an AI‑driven crop prediction system that can assist farmers in selecting suitable crops and planning agricultural activities. The study also highlights the importance of integrating AI with digital agriculture platforms and climate monitoring systems. AI‑based crop prediction models have the potential to reduce uncertainty in agriculture, optimize resource utilization, and support sustainable farming practices.